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a-j show the time series after eliminating various types of noise using the proposed method; the corresponding frequency spectrum is shown in k-o. Due to the lack of noisy data, the result of f is the same as that of the original signal (see a). The time series after eliminating the square wave noise is shown in g, where it can be seen that the profile of the useful signal is similar to that shown in a. The results after suppressing the triangular wave noise and various other types of noise are shown in h,j. It is evident that there are losses around 500 data point and 2900 data point (see red boxes and blue boxes), with fewer losses seen in j. In other words, when there are various different types of noise in the MT data simultaneously, the improvement gained by removing triangular waves through the proposed method is better. i shows the time series after eliminating the impulse noise, which is roughly the same as the noise-free data.
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Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise...
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Context 1
... compared with the triangular wave noise and impulse noise, the improvement of square wave noise achieved by the proposed method is better, with less loss of signals. Figure 6f is the same as that of the original signal (see Figure 6a). The time series after eliminating the square wave noise is shown in Figure 6g, where it can be seen that the profile of the useful signal is similar to that shown in Figure 6a. ...
Context 2
... 6f is the same as that of the original signal (see Figure 6a). The time series after eliminating the square wave noise is shown in Figure 6g, where it can be seen that the profile of the useful signal is similar to that shown in Figure 6a. The results after suppressing the triangular wave noise and various other types of noise are shown in Figure 6h,j. ...
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... With the rapid development of modern signal processing techniques, numerous technologies have been applied in the processing of MT signals. For example, researchers achieved the suppression of strong humanistic noise through wavelet transform [14], [15]. But wavelet denoising is prone to losing low-frequency signals. ...
The magnetotelluric (MT) signals are susceptible to anthropogenic noise and the existing denoising methods have significant shortcomings in low-frequency situations. To address the problem, we propose an innovative denoising approach. It is different from the existing methods that attempt to achieve signal-noise separation through one step. The denoising process is divided into two steps in the proposed approach. The effective low-frequency dominant component and high-frequency component are sequentially extracted through deep learning and dictionary learning. We propose a new deep learning network named DnCNN-GRU which combines the powerful feature extraction capability of Denoising Convolutional Neural Network (DnCNN) and the strong temporal sequence processing ability of Gated Recurrent Unit (GRU), enabling accurate extraction of the low-frequency MT signal. Furthermore, we integrate this network with the K-Singular Value Decomposition (KSVD) dictionary learning to achieve accurately extraction of effective high-frequency components. Tests of synthetic data indicate that our method is the best compared to a series of state-of-the-art (SOTA) algorithms. It is the only method that can completely remove various types and scales of cultural noises while brilliantly preserves both the low and high-frequency signals. In addition, our method is validated on apparent resistivity and phase data and is significantly superior to the commonly used Robust estimation method. These results demonstrate that our method can solve the problem mentioned above and can be a substitute for Robust estimation or remote reference processing.
... Time-series editing is a kind of direct and immediate means to remove strong interference components from the collected data. With the continuous development of modern digital signal processing technology, time-series editing methods have received more attention, and a series of new algorithms have been proposed, including Wavelet transform [12,17], empirical mode decomposition [18], mathematical morphological filtering [15], sparse decomposition [19], and their combinations. With the development of compressed sensing technology, the sparse decomposition is increasingly used in magnetotelluric signal processing since it has little risk of losing effective signals. ...
The noise suppression method based on dictionary learning has shown great potential in magnetotelluric (MT) data processing. However, the constraints used in the existing algorithm’s method need to set manually, which significantly limits its application. To solve this problem, we propose a deep learning optimized dictionary learning denoising method. We use a deep convolutional network to learn the characteristic parameters of high-quality MT data independently and then use them as the constraints for dictionary learning so as to achieve fully adaptive sparse decomposition. The method uses unified parameters for all data and completely eliminates subjective bias, which makes it possible to batch-process MT data using sparse decomposition. The processing results of simulated and field data examples show that the new method has good adaptability and can achieve recognition with high accuracy. After processing with our method, the apparent resistivity and phase curves became smoother and more continuous, and the results were validated by the remote reference method. Our method can be an effective alternative method when no remote reference station is set up or the remote reference processing is not effective.
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... However, SVD has advantages for noisy signals whose noise component amplitude is much larger than that of useful signals. When the energy of the noise component is weaker than that of the useful signal, the improvement of SVD decreases [19], [20]. Fast ICA (FastICA) separates each source information according to each order of data statistics and can distinguish the noise component from the signal component regardless of high-energy or low-energy noise (even full-time noise). ...
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Magnetotelluric (MT) sounding data are easily contaminated by various noise sources, especially noise with a long duration (even full-time noise), which makes it difficult to obtain accurate values when calculating the weight factors of response function, resulting in distortion of the response results. Based on blind source separation theory, fast independent component analysis (FastICA) can separate this kind of noise. However, this method is challenged by the number of separated field sources and the unequal signal amplitude before and after processing. We have developed a novel signal noise separation method, improving the traditional FastICA, where a correlation coefficient is used to compute the number of field sources, and the fast iterative shrinkage threshold algorithm (FISTA) is used to adjust the signal amplitude problem before and after FastICA decomposition. Compared with common field source division methods and the traditional FastICA, the experimental results indicate that our method can separate and remove noise with a long duration, increase the signal-to-noise ratio (SNR) of the data, and improve the MT response curves. Meanwhile, case studies of measured data illustrate that our method obtains a more robust MT response than the conventional robust method and FastICA.
The existing sparse decomposition denoising methods for magnetotelluric (MT) data need to set the iterative stop condition manually, which not only has a large workload and high difficulty, but also easily causes subjective bias. To this end, we propose a new adaptive sparse representation method for MT data denoising. First, the data to be processed is divided into high-quality segments and noisy segments by machine learning algorithm. Then, the characteristic parameters of high-quality segments are calculated, and the boundary value of the characteristic parameters is taken as the threshold. The threshold has two functions, one is as a criterion for signal-to-noise identification, and the other is as an iterative stop condition for subsequent sparse decomposition. Finally, the optimized orthogonal matching pursuit algorithm is used to separate the signal and noise of the noisy segments, and the denoised segments and high-quality segments are combined to obtain the complete denoised MT data. The field data processing results show that this method is a fully automatic and intelligent MT data denoising method. It greatly improves the signal-to-noise ratio and the apparent resistivity-phase curves.
Traditional magnetotelluric (MT) signal processing usually uses time‐frequency transformation method. Wavelet is also a time‐frequency transformation method that used to suppress the MT noise. However, the selection of the threshold is very significant, and the unsuitable threshold will lead to excessive distortion of the reconstructed signal. Thus, we propose a method for MT noise suppression using grey wolf optimized wavelet threshold (GWO‐Th). First, the MT signal is decomposed by wavelet with appropriate wavelet basis and decomposition layers. The generalized cross‐validation (GCV) criterion is used as the fitness function of GWO algorithm, which optimize the threshold of each decomposition layers. Then, the detail coefficients (DCs) of each layer and the maximum layer of the approximation coefficients (ACs) are used in the optimized threshold. Next, the inverse wavelet transform is performed. Finally, the noise contour is obtained through iteratively searching for the optimal threshold, and the useful MT signal is reconstructed. Simulation experiments and measured MT data processing show that the large‐scale interference can be effectively suppressed, and the reconstructed MT signal retains the more abundant low‐frequency of useful information. Compared with the remote reference method (RR) method, Fixed threshold (Fix‐Th) method and Birge‐Massart layered threshold (BM‐Th) method, the proposed method realizes the wavelet denoising with adaptive threshold selection in the MT noise suppression. The results obtained show smoother and more continuous apparent resistivity‐phase curves, which verifies the effectiveness of the optimization. This article is protected by copyright. All rights reserved
With the increasing challenge of delivering quality power to the customers along with maintaining voltage stability in the power system, it is desirable that analysis, detection, compression, characterization, and classification can be performed automatically. Further, figuring out the legitimate cause of a power quality disturbance (PQD) is of paramount importance. Therefore, this paper presents a novel multiscale morphological filter-based deep long short-term memory network (DLSTM) with minimum variance random vector functional link network (MVRVFLN) classifier to diagnose and classify various voltage dip scenarios based on the underlying causes. The novel weighted multiscale averaged triple cascaded combination filter (MATCCF) is a fascinated de-noising technique that is designed using four new different cascaded combinations of the basic morphological operators with optimal scale selection based on ensemble kurtosis (EK) index. With direct input of morphological signals, DLSTM results in automatic computation, data compression, feature extraction, and obviation of gradient dispersion issue. Furthermore, the proposed MVRVFLN classifier shows par classification performance with minimum cross-entropy loss and maximum accuracy in noisy and noise-free environments. Finally, the superiority of this novel MM-DLSTM-MVRVFLN methodology (with 99.92% classification accuracy) is validated by comparing it with several deep neural networks (DNNs).
As magnetotelluric (MT) is an important method for exploring the geoelectrical structure of the underground, it has motivated in-depth research and application by many geophysicists. Nevertheless, due to the influence of the environment, the collected data are interfered with strong humanistic noise, which might result in a loss of their authenticity. To solve the above problems, many time-frequency domain methods have emerged. Based on the advantages of singular value decomposition (SVD) denoising, we propose a method of magnetotelluric noisy data processing based on multiresolution singular value decomposition (MSVD) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), which overcomes the lack of flexibility in the construction of the matrix in SVD data processing. First, we introduce a new signal processing method by generalizing SVD to MSVD to obtain more accurate signal characteristics. Due to the difficulty of matrix selection, we suggest the singular value contribution rate as the standard to determine the suitable Hankel matrix and use MSVD to perform effective decomposition. Second, we propose the ICEEMDAN algorithm for removing impulse noise, which efficiently processes each modal component through adaptively decomposition of different thresholds. Experiments on synthetic and realistic data demonstrate that our proposed method can separate the large-scale contours of the magnetotelluric noisy data and improve the time-domain waveform quality of low-frequency signal. The apparent resistivity-phase curves, coherence and SNR are all obviously promoted.
De-noising magnetotelluric (MT) data has become a key research topic closely related to the application of the MT method itself. Variational mode decomposition (VMD), a newly developed non-recursive signal decomposition method, has been applied to the denoising of MT data. However, some high-frequency noise may remain after VMD denoising. Moreover, the useful low-frequency signal and large-scale noise cannot always be easily separated, resulting in the loss of useful low-frequency signal components during signal reconstruction. To overcome this problem, a new time series editing strategy is proposed, which combines wavelet thresholding (WT) and mathematical morphology filtering (MMF) for further processing of the VMD output. WT is used to remove the remaining high-frequency noise from the retained signal after VMD, while MMF is utilized to extract the useful low-frequency signal components from the signal rejected by VMD. Finally, the WT and MMF outputs are merged to obtain the final denoising results. The proposed method was evaluated using a simulated dataset and two real datasets collected in the Hami Basin of East Tianshan, China. The simulated data tests showed that the proposed method has superior denoising ability compared to other time-domain methods, while the real data study showed that the quality of MT responses obtained from the proposed method is superior to that from conventional methods, especially for the low-frequency band.
